Industrial productivity: technical skills, an underestimated factor

Industrial productivity cannot be reduced to the numerical indicators that measure outcomes;

The technical proficiency of teams determines the stability of industrial processes, adherence to take time, and the reduction of minor stoppages.

A skill that is insufficiently mastered can degrade quality, increase scrap rates, and extend lead times — even with high-performing equipment.

Making skill levels visible enables organisations to objectively assess gaps and safeguard the stages of the manufacturing process.

Cross-referencing production indicators with on-the-ground data improves the analysis of performance variations and supports better decision-making.

Sustainable performance relies as much on teams’ ability to make full use of industrial tools as on the technology itself.

Industrial productivity is often approached through the lens of figures. Dashboards structure decision-making and guide the priorities of industrial companies. Yet one decisive factor is consistently underestimated: the actual technical proficiency of teams on the ground. Whilst data measures an outcome, it does not always account for the quality of process execution.

The Overall Equipment Effectiveness (OEE) rate, scrap rate, volume produced per hour, downtime, and manufacturing cost are the principal benchmarks for measuring the performance of a production line.

This approach makes it possible to identify drops in performance and assess the effectiveness of an investment or process optimisation.

However, these indicators measure above all a global outcome.

Whilst they highlight a gap, a slowdown, or a drop in quality, they do not always explain the precise operational causes. A declining OEE rate may stem from a machine fault, a materials supply issue, or a lack of competence in the execution of a stage within the manufacturing process.

In other words, indicators do not systematically reveal the true quality of work carried out on the ground.

The stability of production processes depends largely on the technical proficiency of the workforce. Knowing precisely how a machine operates, understanding the interactions between materials, being able to interpret a deviation in production data… These are all skills that reduce operational uncertainty.

In practical terms, a higher level of technical competence limits minor stoppages and streamlines the production cycle. It enables teams to adhere to take time (the ideal production duration a company should aim for) and to optimise work organisation without overstretching resources.

The performance of a production tool therefore does not depend solely on its technology, but on the ability of teams to make full use of it.

Industrial productivity is directly linked to the quality of execution. A single underdeveloped skill can lead to handling errors or the incorrect application of manufacturing standards.

The consequences are measurable: rising scrap rates, additional rework, increased costs, and longer lead times.

Conversely, when teams possess a consistent level of competence:

• Production becomes more reliable;
• Parts produced reach conformity more quickly;
• The total number of defects decreases and customer satisfaction improves;
• The average time required to reach the expected volume is reduced, enhancing the company’s ability to respond to market demand.

According to the Future of Jobs Report 2025 by the World Economic Forum, approximately 40% of the skills required in the workplace will change over the coming years, and 63% of employers already report a skills gap within their workforce.

Technical expertise can only become a genuine lever if it is made visible. In many industrial companies, the actual level of competence still relies on informal knowledge: experience, seniority, or internal reputation. Yet without structuring this information, companies cannot effectively manage production and investment decisions.

Formalising a skills matrix makes it possible to:

• Map levels of proficiency by role or by operation;
• Identify critical gaps at a given stage of the manufacturing process;
• Safeguard areas of high added value;
• Objectively assess development needs.

This structured approach reveals the operational reality: who genuinely commands a complex adjustment? Who can work autonomously on a sensitive line? And where do the vulnerabilities lie?

Visibility must be sustained over time through structured skills tracking.

Rigorous tracking makes it possible in particular to:

• Adjust resource allocation according to levels of proficiency;
• Safeguard process continuity during organisational changes;
• Anticipate targeted training needs;
• Inform decisions by cross-referencing production indicators with on-the-ground data.

This gives the company a clear framework for analysing variations in yield or quality. Rather than acting solely on the production tool, it can examine overall operational competence and take appropriate action.